I am a graduate student at UIC studying Industrial Engineering with extensive experience in operations and customer service, with a verifiable track record of initiative, reliability, and achievements within the eCommerce sector.
Through my career at Amazon, I have developed good reporting and analytical skills. I am a capable and consistent problem-solver skilled at prioritizing and managing projects with proficiency.
I am passionate about the supply chain, and I love working with data. During my academics at UIC, I have gained knowledge in Supply Chain, forecasting, data analytics, and predictive analytics.
I am currently looking for opportunities in the field of Supply Chain and analytics.
Please contact me at sughosh.kulkarni@outlook.com to start a conversation.
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Experience
Sr. Investigation Specialist, Amazon India
• Performed analysis of resources, resolves, incoming volume, surplus/deficit, and non-production time by designing and developing a dashboard on Microsoft Excel.
• Delivered timely reports and call-outs at weekly, monthly, and YTD intervals.
• Improved critical operational metric shrinkage of teams from 14.89% to 12.71%.
• Supported as designated secondary reviewer for investigators in complex investigations.
• Trained two groups of 15 investigators and handled overall reporting of the team by investigators to ensure timely submissions.
Investigation Specialist, Amazon India
· Identified trends in fraud during investigations and took measures to save close to $500,000.
· Conducted weekly self-audits to ensure high quality and brought in changes in SOPs by working closely with quality analysts and earned three awards for the best quality performance.
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Projects
Forecasting gold prices using time series analysis and forecasting techniques, UIC
• Developed autoregressive and moving average models to forecast the gold prices on R Studio using 50 years of data. The model was able to predict and evaluated with a mean absolute percentage error (MAPE) of 3.35%.
Neural Network Applications for Public Safety in Chicago, UIC
• Developed a neural network model in Python using publicly available real-time data of over 6M crimes reported in the City of Chicago. The classification model predicts the crime to occur in a neighborhood with an accuracy of 84%. An application of this model is to understand the type of support that different areas need.
Optimization of Sales and Operations Planning, UIC
• Forecasted demand using Winter’s model in R, calculated aggregated production plan for each promotion scenario and suggested optimal decision to increase its profits by $140,000 and reduce the cost of operation.
Detection and classification of Pathological voices, UIC
• Built a classification model using 400 voice samples and trained the model using MFCC feature extraction, Random forest for feature selection, and SVM to construct the classifier model. The model classified with an accuracy of 75%.